Presentation

William Xu

2024-10-05

Slide 1

GDP Per Capita Distribution

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.hist(df['gdp_per_capita'].dropna(), bins=20, color='skyblue')
plt.title('Distribution of GDP per Capita')
plt.xlabel('GDP per Capita')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()

The data shows that countries with higher GDP per capita tend to be more economically developed. Countries like the United States and Switzerland are among the top performers in terms of GDP per capita.

Slide 2

plt.figure(figsize=(10, 6))
plt.scatter(df['gdp_per_capita'], df['life_expectancy'], color='lightgreen', alpha=0.6)
plt.title('Scatter Plot of Life Expectancy vs GDP per Capita')
plt.xlabel('GDP per Capita (USD)')
plt.ylabel('Life Expectancy (Years)')
plt.grid(True)
plt.show()

Life expectancy varies significantly across countries, with developed countries such as Japan and Norway having life expectancies over 80 years.

Slide 3

plt.figure(figsize=(10, 6))
plt.scatter(df['gdp_per_capita'], df['unemployment_rate'], color='blue', alpha=0.7)
plt.title('Scatter Plot of Unemployment Rate vs GDP per Capita')
plt.xlabel('GDP per Capita (USD)')
plt.ylabel('Unemployment Rate (%)')
plt.grid(True)
plt.show()

Unemployment rates differ widely, with some developed countries exhibiting very low unemployment rates while other poor countries struggle with higher levels of unemployment.

Slide 4

Below is a table highlighting key statistics from the dataset analysis.

unemployment_rate gdp_per_capita life_expectancy
count 186.000000 203.000000 209.000000
mean 7.268661 20345.707649 72.416519
std 5.827726 31308.942225 7.713322
min 0.130000 259.025031 52.997000
25% 3.500750 2570.563284 66.782000
50% 5.537500 7587.588173 73.514634
75% 9.455250 25982.630050 78.475000
max 37.852000 240862.182448 85.377000

Concluding Slide

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